scholarly journals Sex classification by resting state brain connectivity

2019 ◽  
Author(s):  
Susanne Weis ◽  
Kaustubh Patil ◽  
Felix Hoffstaedter ◽  
Alessandra Nostro ◽  
B.T. Thomas Yeo ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Brain ◽  
Clear Cut ◽  
Human Connectome Project ◽  
Two Samples ◽  
Imaging Research ◽  
Functional Brain Organization

1AbstractA large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants’ sex can be classified based on spatially specific resting state (RS) brain-connectivity, using two samples from the Human Connectome Project (n1 = 434, n2 = 310) and one fully independent sample from the 1000BRAINS study (n=941). The classifier, which was trained on one sample and tested on the other two, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporo-parietal regions, insula and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

scholarly journals Sex Classification by Resting State Brain Connectivity

2019 ◽  
Vol 30 (2) ◽  
pp. 824-835 ◽  
Author(s):  
Susanne Weis ◽  
Kaustubh R Patil ◽  
Felix Hoffstaedter ◽  
Alessandra Nostro ◽  
B T Thomas Yeo ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Brain ◽  
Clear Cut ◽  
Human Connectome Project ◽  
Machine Learning Approach ◽  
Imaging Research ◽  
Functional Brain Organization

Abstract A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants’ sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

scholarly journals Large-Scale Intrinsic Functional Brain Organization Emerges from Three Canonical Spatiotemporal Patterns

2021 ◽  
Author(s):  
Taylor S Bolt ◽  
Jason Nomi ◽  
Danilo Bzdok ◽  
Catie Chang ◽  
B.T. Thomas Yeo ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Spatiotemporal Patterns ◽  
Brain Organization ◽  
Functional Brain ◽  
Temporal Properties ◽  
Human Connectome Project ◽  
Functional Brain Organization

The characterization of intrinsic functional brain organization has been approached from a multitude of analytic techniques and methods. We are still at a loss of a unifying conceptual framework for capturing common insights across this patchwork of empirical findings. By analyzing resting-state fMRI data from the Human Connectome Project using a large number of popular analytic techniques, we find that all results can be seamlessly reconciled by three fundamental low-frequency spatiotemporal patterns that we have identified via a novel time-varying complex pattern analysis. Overall, these three spatiotemporal patterns account for a wide variety of previously observed phenomena in the resting-state fMRI literature including the task-positive/task-negative anticorrelation, the global signal, the primary functional connectivity gradient and the network community structure of the functional connectome. The shared spatial and temporal properties of these three canonical patterns suggest that they arise from a single hemodynamic mechanism.

scholarly journals The Influence of Heart Rate Variability Biofeedback on Cardiac Regulation and Functional Brain Connectivity

2021 ◽  
Vol 15 ◽  
Author(s):  
Andy Schumann ◽  
Feliberto de la Cruz ◽  
Stefanie Köhler ◽  
Lisa Brotte ◽  
Karl-Jürgen Bär
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Cingulate Cortex ◽  
Brain Regions ◽  
Control Group ◽  
Functional Brain ◽  
Middle Cingulate Cortex

BackgroundHeart rate variability (HRV) biofeedback has a beneficial impact on perceived stress and emotion regulation. However, its impact on brain function is still unclear. In this study, we aimed to investigate the effect of an 8-week HRV-biofeedback intervention on functional brain connectivity in healthy subjects.MethodsHRV biofeedback was carried out in five sessions per week, including four at home and one in our lab. A control group played jump‘n’run games instead of the training. Functional magnetic resonance imaging was conducted before and after the intervention in both groups. To compute resting state functional connectivity (RSFC), we defined regions of interest in the ventral medial prefrontal cortex (VMPFC) and a total of 260 independent anatomical regions for network-based analysis. Changes of RSFC of the VMPFC to other brain regions were compared between groups. Temporal changes of HRV during the resting state recording were correlated to dynamic functional connectivity of the VMPFC.ResultsFirst, we corroborated the role of the VMPFC in cardiac autonomic regulation. We found that temporal changes of HRV were correlated to dynamic changes of prefrontal connectivity, especially to the middle cingulate cortex, the left insula, supplementary motor area, dorsal and ventral lateral prefrontal regions. The biofeedback group showed a drop in heart rate by 5.2 beats/min and an increased SDNN as a measure of HRV by 8.6 ms (18%) after the intervention. Functional connectivity of the VMPFC increased mainly to the insula, the amygdala, the middle cingulate cortex, and lateral prefrontal regions after biofeedback intervention when compared to changes in the control group. Network-based statistic showed that biofeedback had an influence on a broad functional network of brain regions.ConclusionOur results show that increased heart rate variability induced by HRV-biofeedback is accompanied by changes in functional brain connectivity during resting state.

scholarly journals The relationship between spatial configuration and functional connectivity of brain regions revisited

2019 ◽  
Author(s):  
Janine D. Bijsterbosch ◽  
Christian F. Beckmann ◽  
Mark W. Woolrich ◽  
Stephen M. Smith ◽  
Samuel J. Harrison
Keyword(s):  
Functional Connectivity ◽  
Network Structure ◽  
Resting State ◽  
Spatial Configuration ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Spatial Effects ◽  
Functional Brain ◽  
Dual Regression ◽  
The Relationship

AbstractIn our previous paper (Bijsterbosch et al., 2018), we showed that network-based modelling of brain connectivity interacts strongly with the shape and exact location of brain regions, such that cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Here we show that these spatial effects on connectivity estimates actually occur as a result of spatial overlap between brain networks. This is shown to systematically bias connectivity estimates obtained from group spatial ICA followed by dual regression. We introduce an extended method that addresses the bias and achieves more accurate connectivity estimates.Impact statementWe show that functional connectivity network matrices as estimated from resting state functional MRI are biased by spatially overlapping network structure.

scholarly journals 0010 Functional Brain Connectivity Alterations in Restless Legs Syndrome are Modulated by Dopaminergic Medication

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A4-A4
Author(s):  
N Tuovinen ◽  
A Stefani ◽  
T Mitterling ◽  
A Heidbreder ◽  
B Frauscher ◽  
...  
Keyword(s):  
Restless Legs Syndrome ◽  
Resting State ◽  
Brain Connectivity ◽  
Sensory Information ◽  
Brain Regions ◽  
Healthy Controls ◽  
Functional Brain ◽  
Dopaminergic Medication ◽  
Restless Legs ◽  
The Government

Abstract Introduction Functional brain connectivity studies revealed alterations within thalamic, salience, and default mode networks in patients with restless legs syndrome. The objective of this study was to characterize functional connectivity and network topology in a large cohort of patients with restless legs syndrome compared to healthy controls, and to investigate the modulatory effect of dopaminergic treatment upon connectivity. Methods 82 patients with restless legs syndrome (untreated, n=30; on dopaminergic medication, n=42; on alpha-2-delta ligands as mono- or polytherapy combined with dopaminergic medication, n=10) and 82 individually age and gender matched healthy controls were studied with resting state functional MRI. Connectivity of twelve resting-state networks was compared with independent component analysis, and among 410 brain regions with graph theoretical modeling. Results Patients with restless legs syndrome showed significantly higher connectivity within salience (P=0.029), executive (P=0.001), somatomotor (P=0.050), and cerebellar (P=0.041) networks, as well as significantly (P<0.05) lower cerebello-frontal communication compared to healthy controls. Untreated patients had significantly (P<0.05) lower cerebello-parietal communication compared to healthy controls and connectivity between the thalamus and frontal regions were significantly increased in patients on dopaminergic medication compared to untreated patients and healthy controls (P<0.05). Conclusion Networks with higher intra-network connectivity (i.e. salience, executive, somatomotor, cerebellar) and lower between regions connectivity (i.e. cerebello-frontal, cerebello-parietal) in restless legs syndrome correspond to regions associated with attention, response inhibitory control, and processing of sensory information. Dopaminergic medication normalizes the altered cerebello-parietal communication and increases thalamic connectivity to the prefrontal cortex suggesting that these regions are associated with the emergence of symptoms in restless legs syndrome. Support The study was funded by a Grant from Translational Research Fund of the government of Tyrol, Austria, and in-kind resources of the Medical University of Innsbruck.

scholarly journals The influence of heart rate variability biofeedback on cardiac regulation and functional brain connectivity

2020 ◽  
Author(s):  
Andy Schumann ◽  
Feliberto de la Cruz ◽  
Stefanie Köhler ◽  
Lisa Brotte ◽  
Karl-Jürgen Bär
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Anterior Insula ◽  
Control Group ◽  
Functional Brain

AbstractBackgroundHeart rate variability (HRV) biofeedback has a beneficial impact on perceived stress and emotion regulation. However, its impact on brain function is still unclear. In this study, we aimed to investigate the effect of an 8-week HRV-biofeedback intervention on functional brain connectivity in healthy subjects.MethodsHRV biofeedback was carried out in five sessions per week, including four at home and one in our lab. A control group played jump‘n’run games instead of the training. Functional magnetic resonance imaging was conducted before and after the intervention in both groups. To compute resting state functional connectivity (RSFC), we defined regions of interest in the ventral medial prefrontal cortex (VMPFC) and a total of 260 independent anatomical regions for network-based analysis. Changes of RSFC of the VMPFC to other brain regions were compared between groups. Temporal changes of HRV during the resting state recording were correlated to dynamic functional connectivity of the VMPFC.ResultsFirst, we corroborated the role of the VMPFC in cardiac autonomic regulation. We found that temporal changes of HRV were correlated to dynamic changes of prefrontal connectivity, especially to the middle cingulate cortex, left anterior insula, right amygdala, supplementary motor area, dorsal and ventral lateral prefrontal regions. The biofeedback group showed a drop in heart rate by 5.5 beats/min and an increased RMSSD as a measure of HRV by 10.1ms (33%) after the intervention. Functional connectivity of the VMPFC increased mainly to the right anterior insula, the dorsal anterior cingulate cortex and the dorsolateral prefrontal cortex after biofeedback intervention when compared to changes in the control group. Network-based statistic showed that biofeedback had an influence on a broad functional network of brain regions.ConclusionOur results show that increased vagal modulation induced by HRV-biofeedback is accompanied by changes in functional brain connectivity during resting state.

scholarly journals Innate Organization of the Human Brain

2021 ◽  
Author(s):  
M. Fiona Molloy ◽  
Zeynep M. Saygin
Keyword(s):  
Individual Variability ◽  
Adult Brain ◽  
Brain Organization ◽  
Attention Networks ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Differential Gene ◽  
Functional Brain Organization ◽  
High Level ◽  
Genetically Determined

The adult brain is organized into distinct functional networks, forming the basis of information processing and determining individual differences in behavior. Is this network organization genetically determined and present at birth? Here, we use unsupervised learning to uncover intrinsic functional brain organization using resting-state connectivity from a large cohort of neonates (Developing Human Connectome Project). We identified a set of symmetric, hierarchical, and replicable networks: sensorimotor, visual, default mode, ventral attention, and high-level vision. We also quantified neonate individual variability, finding low variability for sensorimotor, but high for ventral attention networks. These neonate networks resembled adult networks (Yeo et al., 2011), but frontoparietal and limbic networks found in adults were indiscernible in neonates. Finally, differential gene expression provided a potential explanation for the emergence of these distinct networks. Our results reveal the basic proto-organization of cortex at birth, but indicate a role for maturation and experience in developing adult-like functional brain organization.

scholarly journals A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

2018 ◽  
Author(s):  
Benjamin A. Seitzman ◽  
Caterina Gratton ◽  
Scott Marek ◽  
Ryan V. Raut ◽  
Nico U.F. Dosenbach ◽  
...  
Keyword(s):  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Functional Network ◽  
Network Organization ◽  
Brain Organization ◽  
Functional Brain ◽  
Network Analyses ◽  
Functional Brain Network ◽  
Functional Brain Organization

AbstractAn important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011 and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

scholarly journals Differences in directed functional brain connectivity related to age, sex and mental health

2019 ◽  
Author(s):  
Martina J. Lund ◽  
Dag Alnæs ◽  
Simon Schwab ◽  
Dennis van der Meer ◽  
Ole A. Andreassen ◽  
...  
Keyword(s):  
Mental Health ◽  
Information Flow ◽  
Psychiatric Symptoms ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Uk Biobank ◽  
Functional Brain ◽  
Brain Connectome ◽  
Human Connectome Project ◽  
Insight Into

AbstractObjectiveFunctional interconnections between brain regions define the ‘connectome’ which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the brain connectome and age, sex, intellectual abilities and mental health.MethodsWe applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank.ResultsOur analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health.DiscussionOur findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.

Association between metabolic syndrome and resting-state functional brain connectivity

2021 ◽  
Author(s):  
Barnaly Rashid ◽  
Victoria N. Poole ◽  
Francesca C. Fortenbaugh ◽  
Michael Esterman ◽  
William P. Milberg ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Resting State ◽  
Brain Connectivity ◽  
Functional Brain
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